High Energy Physics - Phenomenology

Title:
CWoLa Hunting: Extending the Bump Hunt with Machine Learning

Abstract: The oldest and most robust technique to search for new particles is to look
for `bumps' in invariant mass spectra over smoothly falling backgrounds. This
is a powerful technique, but only uses one-dimensional information. One can
restrict the phase space to enhance a potential signal, but such tagging
techniques require a signal hypothesis and training a classifier in simulation
and applying it on data. We present a new method for using all of the available
information (with machine learning) without using any prior knowledge about
potential signals. Given the lack of new physics signals at the Large Hadron
Collider (LHC), such model independent approaches are critical for ensuring
full coverage to fully exploit the rich datasets from the LHC experiments. In
addition to illustrating how the new method works in simple test cases, we
demonstrate the power of the extended bump hunt on a realistic all-hadronic
resonance search in a channel that would not be covered with existing
techniques.